
We propose a novel Pipelined-DAGNN that can effectively speed up the
feature extraction process of DAGNN [1]} and encode
the job information in a set of embedding vectors.

We present a novel scalable deep RL model that feeds an embedding
sequence of vectors to the policy network instead of creating a high-dimensional
flat embedding vector as DeepWeave [2]}, which significantly
reduces the size of the policy network and hence can process DAGs
of arbitrary sizes and shapes.

We propose a novel scheme of incorporating the self-attention mechanism
into the deep RL model to capture the interaction between schedulable
coflows encoded as the embedding sequence, and thus remedy the accuracy
loss in our output policy without packing individual embedding vectors
into a long flat vector.

We evaluate the model's performance using real-life traces and generated
traces. Simulation results show our model outperforms state-of-the-art
solutions in terms of the total weighted JCT.

